# Attribute Artifacts Removal for Geometry-based Point Cloud Compression

arXiv: https://arxiv.org/abs/2112.00560 · HTML: https://mlsanity.com/paper/2112.00560/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 65 references that are in the map.
- First posted on arXiv 1 Dec 2021.

## Builds on (most cited first, 29 of 29)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://mlsanity.com/paper/1606.09375/) (2016)
- [Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising](https://mlsanity.com/paper/1608.03981/) (2016)
- [Spectral Networks and Locally Connected Networks on Graphs](https://mlsanity.com/paper/1312.6203/) (2013)
- [Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules](https://mlsanity.com/paper/2001.01568/) (2020)
- [SO-Net: Self-Organizing Network for Point Cloud Analysis](https://mlsanity.com/paper/1803.04249/) (2018)
- [Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration](https://mlsanity.com/paper/1508.02848/) (2015)
- [Adaptive Graph Convolutional Neural Networks](https://mlsanity.com/paper/1801.03226/) (2018)
- [Compression Artifacts Reduction by a Deep Convolutional Network](https://mlsanity.com/paper/1504.06993/) (2015)
- [Local Spectral Graph Convolution for Point Set Feature Learning](https://mlsanity.com/paper/1803.05827/) (2018)
- [Multiscale Point Cloud Geometry Compression](https://mlsanity.com/paper/2011.03799/) (2020)
- [RGCNN: Regularized Graph CNN for Point Cloud Segmentation](https://mlsanity.com/paper/1806.02952/) (2018)
- [Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression](https://mlsanity.com/paper/1903.08548/) (2019)
- [MFQE 2.0: A New Approach for Multi-frame Quality Enhancement on Compressed Video](https://mlsanity.com/paper/1902.09707/) (2019)
- [Multi-Frame Quality Enhancement for Compressed Video](https://mlsanity.com/paper/1803.04680/) (2018)
- [CAS-CNN: A Deep Convolutional Neural Network for Image Compression Artifact Suppression](https://mlsanity.com/paper/1611.07233/) (2016)
- [Linked Dynamic Graph CNN: Learning on Point Cloud via Linking Hierarchical Features](https://mlsanity.com/paper/1904.10014/) (2019)
- [Relation Graph Network for 3D Object Detection in Point Clouds](https://mlsanity.com/paper/1912.00202/) (2019)
- [Multi-level Wavelet-based Generative Adversarial Network for Perceptual Quality Enhancement of Compressed Video](https://mlsanity.com/paper/2008.00499/) (2020)
- [DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal](https://mlsanity.com/paper/1806.03275/) (2018)
- [One-to-Many Network for Visually Pleasing Compression Artifacts Reduction](https://mlsanity.com/paper/1611.04994/) (2016)
- [Folding-based compression of point cloud attributes](https://mlsanity.com/paper/2002.04439/) (2020)
- [A DenseNet Based Approach for Multi-Frame In-Loop Filter in HEVC](https://mlsanity.com/paper/1903.01648/) (2019)
- [Learning a Single Model with a Wide Range of Quality Factors for JPEG Image Artifacts Removal](https://mlsanity.com/paper/2009.06912/) (2020)
- [Comments on "Compression of 3D Point Clouds Using a Region-Adaptive Hierarchical Transform"](https://mlsanity.com/paper/1805.09146/) (2018)
- [Dynamic Polygon Clouds: Representation and Compression for VR/AR](https://mlsanity.com/paper/1610.00402/) (2016)
- [GLOW : Global Weighted Self-Attention Network for Web Search](https://mlsanity.com/paper/2007.05186/) (2020)

## Built on by (most cited first, 13 of 13)

- [GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute](https://mlsanity.com/paper/2303.13764/) (2023)
- [CARNet:Compression Artifact Reduction for Point Cloud Attribute](https://mlsanity.com/paper/2209.08276/) (2022)
- [Improving 3D Gaussian Splatting Compression by Scene-Adaptive Lattice Vector Quantization](https://mlsanity.com/paper/2509.13482/) (2025)
- [Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression](https://mlsanity.com/paper/2402.11250/) (2024)
- [High Efficiency Wiener Filter-based Point Cloud Quality Enhancement for MPEG G-PCC](https://mlsanity.com/paper/2503.17467/) (2025)
- [PCE-GAN: A Generative Adversarial Network for Point Cloud Attribute Quality Enhancement based on Optimal Transport](https://mlsanity.com/paper/2503.00047/) (2025)
- [Saliency-Aware Spatio-Temporal Artifact Detection for Compressed Video Quality Assessment](https://mlsanity.com/paper/2301.01069/) (2023)
- [RBFIM: Perceptual Quality Assessment for Compressed Point Clouds Using Radial Basis Function Interpolation](https://mlsanity.com/paper/2503.14154/) (2025)
- [Att2CPC: Attention-Guided Lossy Attribute Compression of Point Clouds](https://mlsanity.com/paper/2410.17823/) (2024)
- [STQE: Spatial-Temporal Attribute Quality Enhancement for G-PCC Compressed Dynamic Point Clouds](https://mlsanity.com/paper/2507.17522/) (2025)
- [UGAE: Unified Geometry and Attribute Enhancement for G-PCC Compressed Point Clouds](https://mlsanity.com/paper/2510.23009/) (2025)
- [Rate-distortion Optimized Point Cloud Preprocessing for Geometry-based Point Cloud Compression](https://mlsanity.com/paper/2508.01633/) (2025)
- [DUGAE: Unified Geometry and Attribute Enhancement via Spatiotemporal Correlations for G-PCC Compressed Dynamic Point Clouds](https://mlsanity.com/paper/2603.26183/) (2026)

## Abstract (arXiv)

Geometry-based point cloud compression (G-PCC) can achieve remarkable compression efficiency for point clouds. However, it still leads to serious attribute compression artifacts, especially under low bitrate scenarios. In this paper, we propose a Multi-Scale Graph Attention Network (MS-GAT) to remove the artifacts of point cloud attributes compressed by G-PCC. We first construct a graph based on point cloud geometry coordinates and then use the Chebyshev graph convolutions to extract features of point cloud attributes. Considering that one point may be correlated with points both near and far away from it, we propose a multi-scale scheme to capture the short- and long-range correlations between the current point and its neighboring and distant points. To address the problem that various points may have different degrees of artifacts caused by adaptive quantization, we introduce the quantization step per point as an extra input to the proposed network. We also incorporate a weighted graph attentional layer into the network to pay special attention to the points with more attribute artifacts. To the best of our knowledge, this is the first attribute artifacts removal method for G-PCC. We validate the effectiveness of our method over various point clouds. Objective comparison results show that our proposed method achieves an average of 9.74% BD-rate reduction compared with Predlift and 10.13% BD-rate reduction compared with RAHT. Subjective comparison results present that visual artifacts such as color shifting, blurring, and quantization noise are reduced.